May 2026 performance summary: Delivered stability, compatibility, and data-processing improvements across two repos (apache/iceberg-python and linkedin/openhouse). Key features delivered include a robust deepcopy fix for And/Or/Not expressions in iceberg-python, DataLoader batch-size handling with table transformers in openhouse, and extended literal-type support in DataFusion conversions for filters. In addition, the Python minimum version was broadened to 3.10 to expand adoption while maintaining functionality. These changes reduce runtime errors, improve data throughput, and enable broader customer compatibility.

Summary for 2026-04: Delivered a suite of DataLoader improvements in linkedin/openhouse that significantly boosted stability, performance, and correctness. Key changes include dependency upgrades (li-pyiceberg 0.11.5, DataFusion 53.0.0) with corresponding lockfile updates for compatibility; configurable JVM arguments for JNI/HDFS data loading to enforce user-specified memory usage; reintroduction of ArrivalOrder scan ordering and batch_size with full test coverage; parallel I/O support via files_per_split for multi-file splits; and a DataFusion optimize_scan bug fix leveraging a MappingSchema to correctly alias and quote identifiers. End-to-end tests and verification passed, demonstrating business value through faster, more reliable data loads and fewer runtime errors.

March 2026 performance summary focused on delivering robust data loading, faster query capabilities, and more reliable test infrastructure, with a strong emphasis on reproducibility and business value.

February 2026 — OpenHouse DataLoader delivered foundational architecture and catalog-agnostic loading capabilities, expanded data loading flexibility with filtering, split planning, and column projections, and strengthened typing and CI. These changes enable scalable, reliable ingestion across Iceberg catalogs, reduce integration friction, and improve developer workflow.